Digging ice-capped Arctic depths to understand climate change

Subject :Environment

Section: Climate change

Context:

The heat content of the Arctic Ocean is crucial globally, affecting climate, weather, sea levels, and ecosystems.

Details:

It serves as an indicator of broader climate change effects worldwide, connecting ecosystems, economies, and societies globally.

Arctic study model by IIT Madras:

Researchers from IIT Madras have created an artificial neural network (ANN) model to estimate Ocean Heat Content (OHC) in ice-covered Arctic regions.
They have linked satellite-based sea ice data to in-situ CTD (conductivity, temperature, depth) profiles to estimate OHC up to 700 metres deep.
This model accurately predicts OHC changes and tracks spatio-temporal variations, offering insights into historical trends and regional patterns.

About the Study:

The study uses satellite data products like sea ice concentration, sea ice thickness, surface temperature, ambient air temperatures, and snow depth.
Daily sea ice thickness and surface temperature products from the APP-x product suite were used in the study.
Surface and 2m air temperatures from satellite observations over the Arctic region were utilized.
Snow depth data were collected from the TOPAZ4 reanalysis products.
In combination with the satellite data products, the researchers used data from instruments like the WHOI-ITP, which measures temperature and other properties of the ocean under the ice.
The model is based on theoretical considerations about various factors affecting heat transfer in the region, including:
- Heat advection by Atlantic and Pacific waters,
- Heat exchange at different boundaries (ocean-atmosphere, ocean-continent, ocean-seabed) and
- Sea ice state (thickness, extent, properties).
The model also provides a promising tool for estimating spatial and temporal OHC changes in the ice-covered Arctic and has the potential to be further refined for deeper layers.

Artificial Neural Network (ANN):

ANN is a machine learning technique that learns patterns from data and establishes relationships between inputs and outputs.
They experimented with different configurations of the ANN architecture, including the number of hidden layers, number of neurons, activation functions, and scaling techniques.
The ANN model takes these inputs, processes them through multiple layers, and produces an estimate of OHC change.
A comparison is made between the model-derived OHC values and the OHC values obtained from the Multi Observation Global Ocean ARMOR3D L4 analysis system.

Working of Artificial Neural Network (ANN):

For details of Arctic sea and Arctic council: